All the rendered images are of \(400\times 400\)  resolution with 4 channels (RGB + alpha channel for background). SPARF has three main splits for every 3D shape: training views (400 views), test views (20 views), and an OOD “hard" views (10 views) as can be shown in Figure REF . Regarding the collected SRFs, Plenoxels [1]} is used as the base module. The spherical harmonics dimension of the whole SRFs is \(d=4\times 3= 12\) , while for partial SRFs, it is \(d=1\times 3 = 3\) . Most of the hyperparameters used in optimizing the SRFs are the default ones proposed in the Plenoxels paper [1]} (as can be seen in the attached code under Svox2/opt/opt-py). However, the following hyperparameters were engineered in order to scale up the optimization and maintain the quality of the SRFs ( as can be seen in Figure REF , and REF ). Running Plenoxels [1]} for fewer iterations (3\(\times \) 12K) reduces the time by 30% while maintaining the same PSNR. Using 400 views/shapes in SPARF to optimize the SRFs keep the time manageable in optimization (\(\sim 4\)  minutes for the 512 resolution) while maintaining high PSNR (\(\sim 30\) dB). When saving the SRFs, we only save the set of coordinates (integers) and float features (densities and radiance components). The upsampling iteration of Plenoxels is set to 1\(\times \) 12K for faster convergence. The distribution of the collected dataset is detailed in Table REF . More examples of the whole partial SRFs collected in SPARF can be found in Figure REF . The whole SRFs are easily convertible to high-quality meshes using Marching Cubes [4]} as shown in Figure REF .
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